In [1]}, MAML with implicit gradients (iMAML) was proposed with the goal of reducing the computational cost of MAML by leveraging implicit differentiation. The authors derived an analytical expression for the meta-gradient that depends only on the solution to the inner optimization and not the path taken by the inner optimization algorithm. However, this expression involves matrix inversion that has been approximated by solving a problem that involves using conjugate gradient (CG) method. Though iMAML reduces the computation cost compared to MAML, it still involves high computation cost due to: (i) the two loop procedure, (ii) the hessian computation which could be computationally expensive for large models, and (iii) the matrix inversion procedure using CG. Due to these limitations, running iMAML in a distributed environment that involves local computation and communication using energy-limited IoT devices is not feasible. Moreover, iMAML assumes a large number of labeled tasks since at every iteration, it samples a batch of these tasks, which may not be practical in cases where there are only few tasks owned by different agents. Therefore, in this paper, we propose a distributed and energy-efficient meta-learning approach that overcomes these limitations and ensures efficient learning.
